AI in the Enterprise (the problem)

I was recently chatting to a friend who works as a Data Science consultant in the London Area – and a topic dear to my heart came up. How to successfully do ‘AI’ (or Data Science) in the enterprise. Now I work for an Enterprise SaaS company in the recruitment space, so I’ve got a certain… Continue reading AI in the Enterprise (the problem)

Interview with a Data Scientist: Greg Linden

I caught up with Greg Linden via email recently Greg was one of the first people to work on data science in Industry – he invented the item-to-item collaborative filtering algorithm at Amazon.com in the late 90s. I’ll quote his bio from Linkedin: “Much of my past work was in artificial intelligence, personalization, recommendations, search,… Continue reading Interview with a Data Scientist: Greg Linden

Are RNN’s ready to replace journalists?

I recently was experimenting with RNN’s in Keras. I used the example and edited it slightly. This is what I got for Nietzsche – as you can see the answer above to my question is No. ——– diversity: 0.2 ——- Generating with seed: “iginal text, homo natura; to bring it ab” iginal text, homo natura;… Continue reading Are RNN’s ready to replace journalists?

3 tips for successful Data Science Projects

I’ve been doing Data Science projects, delivering software and doing Mathematical modelling for nearly 7 years (if you include grad school). I really don’t know everything, but these are a few things I’ve learned. Consider this like a ‘joel test‘ for Data Science. Use a reproducible framework like Cookiecutter Data Science. My workflow used to… Continue reading 3 tips for successful Data Science Projects

Where does ‘Big Data’ fit into Procurement?

I spent about a year working as an Energy Analyst in Procurement at a large Telecommunications company. I’m by no means an expert but these are my own thoughts on where I feel ‘big data’ fits into procurement. Firstly for the stake of this argument let us consider procurement as a the purchase of goods… Continue reading Where does ‘Big Data’ fit into Procurement?

A map of the PyData Stack

One question you have when you use Python is what do I do with my data. How do I process it and analyze it. The aim of this flow chart is to simply provide a simple to use ‘map’ of the PyData stack. At PyData Amsterdam I’ll present this and explain it in more detail… Continue reading A map of the PyData Stack

Interview with a Data Scientist: Ivana Balazevic

Ivana Balazevic is a Data Scientist at a Berkeley based startup Wise.io, where she is working in a small team of data scientists on solving problems in customer service for different clients. She did her bachelor’s degree in Computer Science at the Faculty of Electrical Engineering and Computing in Zagreb and she recently finished her… Continue reading Interview with a Data Scientist: Ivana Balazevic

What I’ve been working on – late 2015 and early 2016

I find it useful for morale just to write up what I’ve been working on and what I’ve learned over the last few months. PyMC3: Bayesian Logistic Regression: Bayesian Logistic Regression and Model Selection – I wrote an example of how to use Deviance Information Criterion for model selection in a Bayesian Logistic Regression. This… Continue reading What I’ve been working on – late 2015 and early 2016